DAWN: Enhancing Fake News Detection by Leveraging Engagement Earliness in a Temporality-Aware Setting

Centrala begrepp

Social media engagement patterns, particularly the earliness of engagement, can be a strong indicator of news veracity and can be leveraged to improve fake news detection models, especially in real-world scenarios where temporal information is crucial.

Sammanfattning

• Bibliographic Information: Kim, J., Lee, J., In, Y., Yoon, K., & Park, C. (2025). Revisiting Fake News Detection: Towards Temporality-aware Evaluation by Leveraging Engagement Earliness. In Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining (WSDM ’25), March 10–14, 2025, Hannover, Germany. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3701551.3703524
• Research Objective: This paper addresses the limitations of existing social graph-based fake news detection methods that ignore the temporal dynamics of social media data. The authors propose a novel method called DAWN (Detecting fake news via eArliness-guided reWeightiNg) that leverages the earliness of user engagement with news articles as a key indicator of veracity.
• Methodology: DAWN constructs a social graph where nodes represent news articles and edges represent co-engagement patterns. The method then leverages the concept of "engagement earliness," hypothesizing that users who engage with news shortly after publication are more likely to be influenced by confirmation bias, thus sharing content aligning with their existing beliefs. This earliness information is used to construct edge features, which are then fed into a graph structure learning framework to re-weight edges in the social graph. This re-weighting helps to downplay the impact of "noisy edges" connecting real and fake news articles that are likely a result of later engagements. Finally, a GNN classifier uses the re-weighted graph and node features extracted from the article text to predict the veracity of news articles.
• Key Findings: Through extensive data analysis on two popular fake news datasets (PolitiFact and GossipCop), the authors demonstrate a strong correlation between engagement earliness and the veracity label consistency of news article pairs. They find that earlier engagements tend to connect articles of the same veracity, while later engagements are more likely to link real news with fake news. This finding supports their hypothesis that confirmation bias plays a significant role in early engagements. Furthermore, their experimental results show that DAWN significantly outperforms existing state-of-the-art fake news detection methods, particularly in a temporality-aware evaluation setting that mimics real-world scenarios.
• Main Conclusions: The study highlights the importance of considering temporal information in fake news detection and demonstrates that engagement earliness is a valuable feature for improving model accuracy. The proposed DAWN method offers a promising approach for building more robust and reliable fake news detection systems.
• Significance: This research significantly contributes to the field of fake news detection by proposing a novel and effective method that addresses the limitations of existing approaches. The findings have important implications for developing more accurate and reliable systems for identifying and mitigating the spread of misinformation online.
• Limitations and Future Research: While DAWN shows promising results, the authors acknowledge that the method relies on the availability of social media engagement data, which may not always be accessible. Future research could explore incorporating other temporal features or investigating the generalizability of the approach to other domains and languages.

Statistik

Existing social graph-based fake news detection methods experience a performance drop of up to 8.6%p in F1 score when evaluated in a temporality-aware setting.
DAWN outperforms baseline models by a substantial margin, enhancing accuracy and F1 score by up to 5.6%p and 7.3%p, respectively, on the GossipCop dataset.

Citat

"However, we point out that conventional social graph-based methods are trained and evaluated under an unrealistic scenario"
"In this work, we revisit the training and evaluation setting of social graph-based fake news detection methods, and propose a novel method that is applicable to real-world learning environments in which the temporal information should not be overlooked."
"Our empirical findings indicate that later engagements (e.g., consuming or reposting news) contribute more to noisy edges that link real news-fake news pairs in the social graph."

Viktiga insikter från

Revisiting Fake News Detection: Towards Temporality-aware Evaluation by Leveraging Engagement Earliness

by Junghoon Kim... på arxiv.org 11-21-2024

https://arxiv.org/pdf/2411.12775.pdf

Djupare frågor

How can the concept of engagement earliness be applied to other online platforms and content types beyond news articles, such as videos, images, or forum posts?

The concept of engagement earliness, as explored in the context of fake news detection, holds significant potential for application across various online platforms and content types beyond news articles. The fundamental principle revolves around the observation that early engagement patterns can reveal valuable insights into the nature and credibility of online content. Let's delve into how this concept can be extrapolated to other domains:

Videos (e.g., YouTube, TikTok): Early engagement metrics like initial views, likes, dislikes, and comments within a short timeframe after upload can be analyzed. A surge in engagement, especially with polarized reactions, might indicate potentially controversial or misleading content.
Images (e.g., Instagram, Pinterest): Early likes, shares, saves, and comments on images can be indicative. For example, an image eliciting rapid shares and emotionally charged comments might warrant further scrutiny for potential manipulation or misrepresentation.
Forum Posts (e.g., Reddit, Quora): Early upvotes, downvotes, replies, and shares can be assessed. A post quickly garnering a high volume of polarized responses might suggest the presence of misinformation or inflammatory content.
Adapting to Platform-Specific Nuances:
It's crucial to acknowledge that the specific implementation of engagement earliness would necessitate adaptation based on the unique characteristics of each platform. Factors to consider include:

Platform Norms: Engagement patterns vary across platforms. A surge in early engagement might be typical on a platform like Twitter, known for its fast-paced nature, but unusual on a platform like LinkedIn, geared towards professional networking.
Content Format: The type of content influences engagement behavior. Videos tend to garner more immediate engagement compared to lengthy articles.
User Demographics: The demographics of the platform's user base impact engagement patterns. Platforms with younger audiences might exhibit different early engagement behaviors compared to those with older demographics.
Beyond Binary Classification:
While the research paper focused on fake news detection as a binary classification task, the concept of engagement earliness can extend to more nuanced assessments of online content. For instance, it could be used to identify:

Emerging Trends: Tracking early engagement surges can help detect trending topics or viral content.
Content Quality: High early engagement coupled with positive sentiment might indicate high-quality content.
Potential for Misinformation: Monitoring early engagement patterns can serve as an early warning system for potentially misleading or harmful content, enabling platforms to take preemptive measures.
In conclusion, the concept of engagement earliness offers a valuable lens through which to analyze online content across diverse platforms. By adapting the specific metrics and thresholds to the unique context of each platform and content type, we can leverage this concept to enhance content moderation, combat misinformation, and foster a healthier online environment.

Could the emphasis on early engagement inadvertently bias the model towards news sources that prioritize rapid dissemination of information, even if their content is not always accurate?

You raise a valid and crucial concern. An over-reliance on early engagement as a primary indicator of fake news could indeed introduce unintended biases, particularly favoring sources that prioritize speed over accuracy. Here's a breakdown of the potential pitfalls:

Amplifying Sensationalism: Sources known for clickbait headlines and sensationalized content often generate rapid, emotionally charged engagement. A model heavily reliant on early signals might misinterpret this engagement as an indicator of credibility, inadvertently promoting such sources.
Rewarding Network Effects: Established news outlets with large, active followings benefit from inherent network effects. Their content tends to spread rapidly due to pre-existing distribution channels and audience trust, potentially overshadowing credible but less established sources.
Penalizing In-Depth Reporting: Investigative journalism and well-researched articles often require time to produce and might not elicit immediate, widespread engagement. An overemphasis on early signals could undervalue such content, even if it's ultimately more accurate and insightful.
Mitigating Bias and Promoting Balanced Evaluation:
To address these concerns and ensure a more balanced evaluation of news sources, it's essential to:

Incorporate Content Analysis: Content-based features, such as linguistic cues, source verification, and fact-checking, should be integrated alongside engagement signals. This helps distinguish between genuine engagement and engagement driven by manipulation or sensationalism.
Consider Temporal Dynamics: Instead of solely focusing on the initial burst of engagement, analyze how engagement patterns evolve over time. Sustained engagement from diverse user groups might be a more reliable indicator of credibility than a fleeting surge.
Factor in Source Reputation: Incorporate source reputation data from fact-checking organizations or established media credibility rankings. This helps contextualize engagement patterns and identify sources with a history of spreading misinformation.
Promote Algorithmic Transparency: Transparency in how algorithms weigh different factors is crucial. This allows for scrutiny, accountability, and ongoing refinement to minimize bias and ensure fairness.
In essence, while engagement earliness offers valuable insights, it should be treated as one piece of a larger puzzle. A robust fake news detection system must consider a multifaceted approach that encompasses content analysis, temporal dynamics, source reputation, and algorithmic transparency to mitigate bias and promote a more informed and discerning online news consumption landscape.

If our understanding of human behavior and social dynamics continues to evolve, how can fake news detection models be designed to adapt and remain effective over time?

The ever-evolving nature of human behavior and social dynamics poses a significant challenge for fake news detection models. As we gain deeper insights into how misinformation spreads and evolves, it's crucial to design models capable of adapting and remaining effective over time. Here are key strategies:
1. Continuous Learning and Adaptation:

Dynamic Model Updates: Implement systems that continuously learn from new data and update their understanding of evolving patterns. This could involve retraining models periodically or employing online learning techniques that adapt in real-time.
Concept Drift Detection: Incorporate mechanisms to detect shifts in language use, emerging tactics of misinformation, and changing social media trends. This allows models to recognize when their existing knowledge might be outdated and trigger adaptation processes.
2. Incorporating Behavioral and Social Science Expertise:

Interdisciplinary Collaboration: Foster collaboration between computer scientists, social scientists, psychologists, and communication experts. Integrating insights from these fields helps models better understand the underlying motivations, biases, and social dynamics that contribute to the spread of misinformation.
Incorporating Psychological Factors: Integrate psychological factors like cognitive biases, emotional reasoning, and social identity into model design. This enables a more nuanced understanding of how individuals engage with and spread misinformation.
3. Leveraging Explainability and Human-in-the-Loop Systems:

Explainable AI (XAI): Develop models that can provide understandable explanations for their predictions. This transparency allows human analysts to identify potential biases, understand model limitations, and make informed decisions.
Human-in-the-Loop: Integrate human expertise into the loop, particularly for complex or ambiguous cases. This could involve fact-checkers verifying model predictions or social media experts providing context and insights.
4. Adapting to Platform Evolution:

Platform-Specific Models: Recognize that each platform has unique characteristics and adapt models accordingly. This might involve training separate models for different platforms or incorporating platform-specific features.
Monitoring Platform Changes: Stay abreast of platform policy changes, algorithm updates, and emerging features that could impact the spread of misinformation. Adapt models to account for these changes and maintain effectiveness.
5. Fostering a Culture of Critical Thinking:

Media Literacy Initiatives: Promote media literacy and critical thinking skills among users. This empowers individuals to evaluate information sources, identify misinformation, and make informed decisions.
Collaborative Fact-Checking: Encourage collaborative fact-checking initiatives where users can contribute to verifying information and flagging potential misinformation.
In conclusion, combating fake news is an ongoing arms race. By embracing continuous learning, interdisciplinary collaboration, explainability, and adaptation to platform evolution, we can develop fake news detection models that remain effective and contribute to a more informed and resilient online information ecosystem.